Assessing the Impact and Underlying Pathways of Sequenced AI feedback on Student Learning
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| Format: | Preprint |
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2026
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| author | Cao, Jie Zhao, Chloe Qianhui Schunn, Christian McLaughlin, Elizabeth A. Lin, Jionghao Koedinger, Kenneth R. |
| author_facet | Cao, Jie Zhao, Chloe Qianhui Schunn, Christian McLaughlin, Elizabeth A. Lin, Jionghao Koedinger, Kenneth R. |
| contents | Feedback is essential for learning, but its effectiveness relies heavily on how well it engages students in the educational process. Generative AI offers novel opportunities to efficiently produce rich, formative feedback, ranging from direct explanations to incrementally sequenced scaffolding designed to promote learner autonomy. Despite these capabilities, it is still unclear whether sequenced (layered) AI feedback -- which provides encouragement and hints before revealing the correct answer -- genuinely enhances engagement and learning outcomes. To investigate this, we randomly assigned 199 participants to receive either sequenced or non-sequenced AI-generated feedback. We evaluated its impact on learning performance, cognitive and behavioral engagement, and affective perceptions to understand how these factors mediate overall learning outcomes. Results show that sequenced feedback elicited slightly higher behavioral engagement and, as anticipated, was perceived as more encouraging and supportive of student independence. Concurrently, however, it induced a higher level of mental effort. Mediation analyses identified a positive affective pathway driven by perceived encouragement, which was completely counteracted by a negative behavioral pathway associated with the average number of tasks requiring three or more submissions; the cognitive pathway (mental effort) remained non-significant. Overall, sequenced feedback led to significantly poorer learning outcomes when compared to direct, non-sequenced feedback. These findings highlight a crucial trade-off: although sequenced AI scaffolding boosts engagement and positive user perceptions, it can have a detrimental effect on actual learning performance. By integrating analyses of outcomes, perceptions, and underlying mechanisms, this study provides nuanced insights for designing automated, AI-driven feedback systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_07469 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Assessing the Impact and Underlying Pathways of Sequenced AI feedback on Student Learning Cao, Jie Zhao, Chloe Qianhui Schunn, Christian McLaughlin, Elizabeth A. Lin, Jionghao Koedinger, Kenneth R. Human-Computer Interaction Feedback is essential for learning, but its effectiveness relies heavily on how well it engages students in the educational process. Generative AI offers novel opportunities to efficiently produce rich, formative feedback, ranging from direct explanations to incrementally sequenced scaffolding designed to promote learner autonomy. Despite these capabilities, it is still unclear whether sequenced (layered) AI feedback -- which provides encouragement and hints before revealing the correct answer -- genuinely enhances engagement and learning outcomes. To investigate this, we randomly assigned 199 participants to receive either sequenced or non-sequenced AI-generated feedback. We evaluated its impact on learning performance, cognitive and behavioral engagement, and affective perceptions to understand how these factors mediate overall learning outcomes. Results show that sequenced feedback elicited slightly higher behavioral engagement and, as anticipated, was perceived as more encouraging and supportive of student independence. Concurrently, however, it induced a higher level of mental effort. Mediation analyses identified a positive affective pathway driven by perceived encouragement, which was completely counteracted by a negative behavioral pathway associated with the average number of tasks requiring three or more submissions; the cognitive pathway (mental effort) remained non-significant. Overall, sequenced feedback led to significantly poorer learning outcomes when compared to direct, non-sequenced feedback. These findings highlight a crucial trade-off: although sequenced AI scaffolding boosts engagement and positive user perceptions, it can have a detrimental effect on actual learning performance. By integrating analyses of outcomes, perceptions, and underlying mechanisms, this study provides nuanced insights for designing automated, AI-driven feedback systems. |
| title | Assessing the Impact and Underlying Pathways of Sequenced AI feedback on Student Learning |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2604.07469 |